5632 research outputs found
Sort by
Differential processing of a chemosensory cue across life stages sharing the same valence state in Caenorhabditis elegans
No full textMany chemosensory cues evoke responses of the same valence under widely varying physiological conditions. It remains unclear whether similar or distinct neural mechanisms are involved in the detection and processing of such chemosensory cues across contexts. We show that in Caenorhabditis elegans, a chemosensory cue is processed by distinct neural mechanisms at two different life stages that share the same valence state. Both starved adults and dauer larvae are attracted to carbon dioxide (CO₂), but CO₂ evokes different patterns of neural activity and different motor outputs at the two life stages. Moreover, the same interneuron within the CO₂ microcircuit plays a different role in driving CO₂-evoked motor output at the two life stages. The dauer-specific patterns of CO₂-evoked activity in this interneuron require a dauer-specific gap junction complex and insulin signaling. Our results demonstrate that functionally distinct microcircuits are engaged in response to a chemosensory cue that triggers the same valence state at different life stages, revealing an unexpected complexity to chemosensory processing
Alcohol, cannabis, and nicotine use have distinct associations with COVID-19 pandemic-related experiences: An exploratory Bayesian network analysis across two timepoints
No full textBackground. Substance use trends during the COVID-19 pandemic have been extensively documented. However, relatively less is known about the associations between pandemic-related experiences and substance use.
Method. In July 2020 and January 2021, a broad U.S. community sample (N = 1123) completed online assessments of past month alcohol, cannabis, and nicotine use and the 92-item Epidemic-Pandemic Impacts Inventory, a multidimensional measure of pandemic-related experiences. We examined links between substance use frequency, and pandemic impact on emotional, physical, economic, and other key domains, using Bayesian Gaussian graphical networks in which edges represent significant associations between variables (referred to as nodes). Bayesian network comparison approaches were used to assess the evidence of stability (or change) in associations between the two timepoints.
Results. After controlling for all other nodes in the network, multiple significant edges connecting substance use nodes and pandemic-experience nodes were observed across both time points, including positive- (r range 0.07 to 0.23) and negative-associations (r range -0.25 to -0.11). Alcohol was positively associated with social and emotional pandemic impacts and negatively associated with economic impact. Nicotine was positively associated with economic impact and negatively associated with social impact. Cannabis was positively associated with emotional impact. Network comparison suggested these associations were stable across the two timepoints.
Conclusion. Alcohol, nicotine, and cannabis use were linked to a few specific domains among a broad range of pandemic-related experiences. Given the cross-sectional nature of these analyses with observational data, further investigation is needed to identify potential causal links
Shock-recovered maskelynite indicates low-pressure ejection of shergottites from Mars
No full textDiaplectic feldspathic glass, commonly known as maskelynite, is a widely used impact indicator, notably for shergottites, whose shock conditions are keys to their geochemistry and launch mechanism. However, classic reverberating shock recovery experiments show maskelynitization at higher shock pressures (>30 gigapascals) than the stability field of the high-pressure minerals found in many shergottites (15 to 25 gigapascals). Most likely, differences between experimental loading paths and those appropriate for martian impacts have created this ambiguity in shergottite shock histories. Shock reverberation yields lower temperature and deviatoric stress than single-shock planetary impacts at equivalent pressure. We report the Hugoniot equation of state of a martian analog basalt and single-shock recovery experiments, indicating partial-to-complete maskelynitization at 17 to 22 gigapascals, consistent with the high-pressure minerals in maskelynitized shergottites. This pressure explains the presence of intact magmatic accessory minerals, used for geochronology in shergottites, and offers a new pressure-time profile for modeling shergottite launch, likely requiring greater origin depth
Model exploration in gravitational-wave astronomy with the maximum population likelihood
No full textHierarchical Bayesian inference is an essential tool for studying the population properties of compact binaries with gravitational waves. The basic premise is to infer the unknown prior distribution of binary black hole and/or neutron star parameters such component masses, spin vectors, and redshift. These distributions shed light on the fate of massive stars, how and where binaries are assembled, and the evolution of the Universe over cosmic time. Hierarchical analyses model the binary black hole population using a prior distribution conditioned on hyperparameters, which are inferred from the data. However, a misspecified model can lead to faulty astrophysical inferences. In this paper we answer the question: given some data, which prior distribution—from the set of all possible prior distributions—produces the largest possible population likelihood? This distribution (which is not a true prior) is π (pronounced “pi stroke”), and the associated maximum population likelihood is L (pronounced “L stroke”). The structure of π is a linear superposition of delta functions, a result which follows from Carathéodory's theorem. We show how π and L can be used for model exploration/criticism. We apply this L formalism to study the population of binary black hole mergers observed in the LIGO-Virgo-KAGRA Collaboration's third gravitational-wave transient catalog. Based on our results, we discuss possible improvements for gravitational-wave population models
Efficient gravitational wave searches with pulsar timing arrays using Hamiltonian Monte Carlo
No full textPulsar timing arrays (PTAs) detect low-frequency gravitational waves (GWs) by looking for correlated deviations in pulse arrival times. Current Bayesian searches use Markov chain Monte Carlo (MCMC) methods, which struggle to sample the large number of parameters needed to model the PTA and GW signals. As the data span and number of pulsars increase, this problem will only worsen. An alternative Monte Carlo sampling method, Hamiltonian Monte Carlo (HMC), utilizes Hamiltonian dynamics to produce sample proposals informed by first-order gradients of the model likelihood. This in turn allows it to converge faster to high dimensional distributions. We implement HMC as an alternative sampling method in our search for an isotropic stochastic GW background, and show that this method produces equivalent statistical results to similar analyses run with standard MCMC techniques, while requiring 100–200 times fewer samples. We show that the speed of HMC sample generation scales as O(N^(5/4)_psr) where N_psr is the number of pulsars, compared to O(N^2_psr) for MCMC methods. These factors offset the increased time required to generate a sample using HMC, demonstrating the value of adopting HMC techniques for PTAs
Andreev reflection spectroscopy in strongly paired superconductors
No full textMotivated by recent experiments on low-carrier-density superconductors, including twisted multilayer graphene, we study signatures of the BCS-to-BEC evolution in Andreev reflection spectroscopy. We establish that in a standard quantum point contact geometry, Andreev reflection in a BEC superconductor is unable to mediate a zero-bias conductance beyond e²/h per lead channel. This bound is shown to result from a mapping that links the subgap conductance of BCS and BEC superconductors. We then demonstrate that sharp signatures of BEC superconductivity, including perfect Andreev reflection, can be recovered by tunneling through a suitably designed potential well. We propose various tunneling spectroscopy setups to experimentally probe this recovery
Nanocluster superstructures assembled via surface ligand switching at high temperature
No full textSuperstructures with nanoscale building blocks, when coupled with precise control of the constituent units, open opportunities in rationally designing and manufacturing desired functional materials. Yet, synthetic strategies for the large-scale production of superstructures are scarce. We report a scalable and generalized approach to synthesizing superstructures assembled from atomically precise Ce₂₄O₂₈(OH)₈ and other rare-earth metal-oxide nanoclusters alongside a detailed description of the self-assembly mechanism. Combining operando small-angle X-ray scattering, ex situ molecular and structural characterizations, and molecular dynamics simulations indicates that a high-temperature ligand-switching mechanism, from oleate to benzoate, governs the formation of the nanocluster assembly. The chemical tuning of surface ligands controls superstructure disassembly and reassembly, and furthermore, enables the synthesis of multicomponent superstructures. This synthetic approach, and the accurate mechanistic understanding, are promising for the preparation of superstructures for use in electronics, plasmonics, magnetics and catalysis
Sharp inequalities for coherent states and their optimizers
No full textWe are interested in sharp functional inequalities for the coherent state transform related to the Wehrl conjecture and its generalizations. This conjecture was settled by Lieb in the case of the Heisenberg group, Lieb and Solovej for SU(2), and Kulikov for SU(1, 1) and the affine group. In this article, we give alternative proofs and characterize, for the first time, the optimizers in the general case. We also extend the recent Faber-Krahn-type inequality for Heisenberg coherent states, due to Nicola and Tilli, to the SU(2) and SU(1, 1) cases. Finally, we prove a family of reverse Hölder inequalities for polynomials, conjectured by Bodmann
Upgrades to W. M. Keck observatory detector systems
No full textWe report on our plans to upgrade the detector systems in the 2022–2024 time frame for three of the workhorse instruments (NIRC2, DEIMOS, and NIRES) operated by the W. M. Keck Observatory. The upgrades are done in collaboration with Observatory partner institutions and other Maunakea observatories. The main motivating factors behind these upgrades are to tackle obsolescence of hardware and software components, to boost observing efficiency, to enhance the instrument throughput, and to add new observing functionality
Alternating Phase Langevin Sampling with Implicit Denoiser Priors for Phase Retrieval
No full textPhase retrieval is the nonlinear inverse problem of recovering a true signal from its Fourier magnitude measurements. It arises in many applications such as astronomical imaging, X-Ray crystallography, microscopy, and more. The problem is highly ill-posed due to the phase-induced ambiguities and the large number of possible images that can fit to the given measurements. Thus, there’s a rich history of enforcing structural priors to improve solutions including sparsity priors and deep-learning-based generative models. However, such priors are often limited in their representational capacity or generalizability to slightly different distributions. Recent advancements in using denoisers as regularizers for non-convex optimization algorithms have shown promising performance and generalization. We present a way of leveraging the prior implicitly learned by a denoiser to solve phase retrieval problems by incorporating it in a classical alternating minimization framework. Compared to performant denoising-based algorithms for phase retrieval, we showcase competitive performance with Fourier measurements on in-distribution images and notable improvement on out-of-distribution images